Multiclass classification of whole‐body scintigraphic images using a self‐defined convolutional neural network with attention modules
Open Access
- 28 August 2021
- journal article
- research article
- Published by Wiley in Medical Physics
- Vol. 48 (10), 5782-5793
- https://doi.org/10.1002/mp.15196
Abstract
Purpose : A self-defined convolutional neural network is developed to automatically classify whole-body scintigraphic images of concern (i.e., the normal, metastasis, arthritis, and thyroid carcinoma), automatically detecting diseases with whole-body bone scintigraphy. Methods : A set of parameter transformation operations are first used to augment the original dataset of whole-body bone scintigraphic images. A hybrid attention mechanism including the spatial and channel attention module is then introduced to develop a deep classification network, Dscint, which consists of eight weight layers, one hybrid attention module, two normalization modules, two fully connected layers, and one softmax layer. Results : Experimental evaluations conducted on a set of whole-body scintigraphic images show that the proposed deep classification network, Dscint, performs well for automated detection of diseases by classifying the images of concerns, with achieving the accuracy, precision, recall, specificity, and F-1 score of 0.9801, 0.9795, 0.9791, 0.9933, and 0.9792, respectively, on the test data in the augmented dataset. A comparative analysis of Dscint and several classical deep classification networks (i.e., AlexNet, ResNet, VGGNet, DenseNet, and Inception-v4) reveals that our self-defined network, Dscint, performs best on classifying whole-body scintigraphic images on the same dataset. Conclusions : The self-defined deep classification network, Dscint, can be utilized to automatically determine whether a whole-body scintigraphic image either is normal or contains diseases of concerns. Specifically, better performance of Dscint is obtained on images with lesions that are present in relatively fixed locations like thyroid carcinoma than those with lesions occurring in non-fixed locations of bone tissue. This article is protected by copyright. All rights reservedKeywords
Funding Information
- Fundamental Research Funds for the Central Universities (31920210013)
- Natural Science Foundation of Gansu Province (20JR5RA511)
- National Natural Science Foundation of China (61562075)
This publication has 15 references indexed in Scilit:
- Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks applicationPLOS ONE, 2020
- Efficient Bone Metastasis Diagnosis in Bone Scintigraphy Using a Fast Convolutional Neural Network ArchitectureDiagnostics, 2020
- Automated diagnosis of bone metastasis based on multi-view bone scans using attention-augmented deep neural networksMedical Image Analysis, 2020
- A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body ScansApplied Sciences, 2020
- CBAM: Convolutional Block Attention ModulePublished by Springer Science and Business Media LLC ,2018
- Generative Adversarial Networks: An OverviewIEEE Signal Processing Magazine, 2018
- Densely Connected Convolutional NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Diagnostic methods for detection of bone metastasesContemporary Oncology, 2017
- Deep Residual Learning for Image RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Bone scintigraphy: procedure guidelines for tumour imagingEuropean Journal of Nuclear Medicine and Molecular Imaging, 2003